Field of the Invention
The present invention relates generally to medical imaging and, more specifically, to an X-ray Computed Tomography (CT) scanning device and method of operating same.
Brief Description of the Background Art
X-ray CT techniques have been widely explored and are often prescribed for clinical applications that include perfusion imaging, image-guided biopsy needle, image-guided intervention, and radiotherapy. In interventional imaging, a patient may undergo one or more imaging studies prior to acquisition of an image. In other cases, such as daily Cone Beam CT (CBCT) examinations for target localization in Image-Guided Radiation Therapy (IGRT), repeated scans are performed as a routine procedure.
The benefits of repeated CT scans are undermined by risks associated with cumulative radiation dosing, and associated patient health concerns. It is known that reducing X-ray tube current and/or shortening exposure time (mAs) in a CT scan is a simple and cost-effective method to reduce potentially harmful radiation exposure. However, the associated image will suffer from serious noise induced artifacts due to the excessive quantum noise, absent adequate treatment on noisy measurement.
Various techniques have been investigated, including optimal scan protocols and advanced image reconstruction algorithms, to reduce radiation dose in CT scans. Statistical Iterative Reconstruction (SIR) methods, which take into account statistical noise properties and accommodate imaging geometry, have shown great potential to reduce quantum noise and artifacts as compared with the current gold standard Filtered Back-Projection (FBP) reconstruction method. A major drawback of the SIR methods is the computational burden associated with multiple re-projection and back-projection operation cycles through the image domain. However, development of fast computers and dedicated hardware allow modified SIR methods to be used in advanced CT equipments.
Generally, the SIR methods can be derived from a Maximum A Posteriori (MAP) estimator given observed-data or measurement, which usually consist of two terms in an associative objective function. The first term, i.e., the data-fidelity term, models the statistical measurement. The second term, i.e., the image prior or regularization term, penalizes the solution. The data-fidelity term incorporating an accurate statistical modeling of the measurement is a prerequisite of the SIR algorithms and an edge-preserving regularization term plays an important role in successful image reconstruction. Usually, the regularization term is chosen as a shift-invariant function that penalizes the differences among local neighboring pixels. These regularizations, through equally smoothing of both noise and edge details, often tend to produce an unfavorable over-smoothing effect. In contrast to the smooth regularization, edge-preserving regularizations/priors have been proposed, e.g., the Huber prior, which replaces the quadratic penalty function with a non-quadratic penalty function that increases less rapidly compared with the quadratic penalty function for sufficiently large arguments. However, these edge-preserving regularizations/priors mostly rely on properties of local smoothness or edges, and do not consider the basic affinity structure information of a desired image, such as the gray levels, edge indicator, dominant direction, and dominant frequency.
To address the aforementioned issues of conventional regularizations/priors, an edge-preserving NonLocal (NL) prior was proposed for CT and Positron Emission Tomography (PET) image reconstructions that fully exploit density difference information, NL connectivity and continuity information of the desired image. With regard to the repeated CT scans, a previously scanned high-quality diagnostic CT image volume usually contains same anatomical information with the current scan except for some anatomical changes due to internal motion or patient weight change. Generally, the CT scans at different times are independently considered, without a systematic attempt to integrate valuable patient-specific prior knowledge, i.e., to integrate previous scanned data, which contains a wealth of prior anatomical, patient-specific information, to promote subsequent imaging process. Performing a low-dose protocol in the follow-up CT scan by fully using the previous image in the current image reconstruction framework is a topic addressed by Chen et al., which proposed a Prior Image Constrained Compressed Sensing (PICCS) algorithm to enable view angle under-sampling by integrating a prior image into the reconstruction procedure. See, Time-Resolved Interventional Cardiac C-arm Cone-Beam CT: An Application of the PICCS Algorithm, IEEE Trans. Med. Imaging, vol. 31, no. 4, pp. 907-923, April 2012. Lauzier extended the PICCS algorithm to the DR-PICCS algorithm for CT radiation dose reduction using a statistical model. See, Characterization of Statistical Prior Image Constrained Compressed Sensing (PICCS): IL Application to Dose Reduction, Med. Phys., Vol. 40, no. 2, pp. 021902:1-14, February 2013. A weakness of PICCS is that prior and current images are taken at the same global geometrical coordinates. This assumption, however, does not necessarily translate into practical settings like the IGRT applications.
Accurate registration and voxel consistency may limit wide use of the PICCS algorithm. To address this issue, Ma, et al. proposed a low-dose CT image filtering method, i.e., an ndiNLM algorithm, utilizing a high-quality normal-dose scan as priori information to perform current low-dose CT image restoration based on NL means criteria. See, J. Ma, at al, Low-dose computed tomography image restoration using previous normal-dose scan, Med. Phys., Vol. 38, no. 10, pp. 5713-5731, October 2011, and A. Buades, et al., A Nonlocal Algorithm for Image Denoising, Proc. IEEE Computer Vision and Pattern Recognition, 2005, Vol. 2, pp. 60-65. The ndiNLM algorithm performed well in noise reduction, but actually is a post-processing approach that does not consider statistical properties of the CT projection data. Another strategy that relaxes image registration is a dictionary learning based approach proposed by Xu et al., which was derived with the consideration of sparse representation theory via a sparse linear combination of the patch-based atoms in the dictionary.